Project Management

If you have been following tech media lately, chances are you’ve heard a lot of chatter about artificial intelligence (AI). Many see it as the way forward to the future. At the same time, on the other side of the divide, some have their apprehensions and even dread the effects these technologies are imposing on society, the arts, and even workplaces.

As fate has it, the hype around AI has meant a lot of things to many people, and seemingly all of them are crossed. The survey, recently conducted by Futures Company known as Fortray, revealed that 83 percent of the people affirmed that artificial intelligence (AI) will transform their line of work in the next three years. Similarly, slightly fewer of them opined that some of their work can be automated through AI. Specifically, 67 percent of the respondents said they were exhilarated, while 88 percent said they were skeptical.

However, AI is nothing that needs to be associated with horror. It can be your most valuable team, especially if you are a project manager and your subordinates have grown used to specific rules of behavior. The use of AI in project management has been increasing daily, and it seems to be going upward, which can help teams make better decisions and proceed at a better pace.

Project management and AI

Project Management AI refers to a system capable of managing projects and coordinating them without being prompted by humans. In this case, it will automate simple tasks and acquire reconnaissance of the project’s KPIs. Project management AI can then leverage that understanding to identify and discover, perform more sophisticated tasks, suggest and advise, and decide—things people often can’t do now. In the long run, an AI system is time-saving and guarantees satisfaction in project and team outcomes.

Project management AI, in this regard, offers a maturity of service that is higher than many of the bots existing today. For instance, a simple HipChat bot that enables a user to check the status of a JIRA task at a glance, although functional, is not an instance of project management AI. Likewise, although it is quite fascinating, the algorithm that incorporates machine learning to predict estimates for tasks cannot be considered AI. That is why it is only possible to think about the real potential when you combine bots and project management algorithms with AI.

Today: What it has for project managers?

The first application of AI in project management will be a project assistant specializing in a specific area related to managing a project or team. Thus, project management AI refers to the AI supporting a team in pursuing a particular goal instead of handling all the issues considered while managing a project means that project management AI will benefit the teams rather than wait.

Fortray, a UK-based IT services and education provider, has begun by supporting estimate, budget, and sprint-related questions. Other companies are geared towards helping with the management of team knowledge. In these specific and limited fields, these early project management AI tools present the world of AI that can help automate tasks, deliver information, and even engage in conversations with project members. Thus, there are quite a few issues. Such early and specific project management AI tools presume that people will feed the data accurately, refresh tools as required, and correct them whenever necessary. The limited capabilities also mean that humans are still a step ahead — at least symbolically — of the AI systems they are developing. Thus, AI technology required for the precise management of projects should evolve more to help users find even more value in the tool.

Second generation: expanding project understanding

The next transformation for such narrow assistants is to begin seeing projects and teams as their next area of knowledge expansion. While at Fortray, we initially had estimates, actuals, sprints, and budgets to manage, but now we should go further and work on the information that can be obtained from task descriptions. If you connect the overall sprint history to people’s performance, you can prove that other tasks interrupt your key engineer every week. When assistants’ concept knowledge increases, new yardsticks for quality, performance, learning, change, and effort will emerge, which were not available before. For example, AI will be aware of the modifications made to source code and relate the changes to people and completed tasks.

This will enable the fixation of bugs reported to a line of code, the person who wrote the code, and the associated functions. This will make it possible to get real, practical signs of the team’s and, more crucially, the project’s performance. As more information accumulates about projects, predictions will become even more accurate, relevant, and understandable to the human mind. However, even this enhanced understanding will still require one more thing: The situation must be developed to transform it into something helpful for accomplishing tasks with usable data

Third generation: filling in the data gaps

The problem, which is rarely discussed in the case of AI and organizational tools like project management software, is the quality and relevance of data. It is a fact that some teams virtually do not write anything at all into their project management tools. Even the most disciplined teams have problems with how machines recognize their data – perhaps the names of the tasks are provided differently, or some of the records are filled with more data and some less. Whether there are specific reasons or simply dealing with a more mature team, it is almost a guarantee that there is always a subset of data that will be either unused or not cleanly appropriately entered in the chosen project management system or toolset.

This is logically proved since data size has always been considered as one of the problems, but at the same time, it can be quickly passed through. However, there is always something that state-of-the-art Machine Learning techniques can bring even if the project is less than 1,000 tasks, especially if you realize it does work when you apply it to the 100 other projects that contain 1,000 tasks. Project management AI can deal with the data challenge by:

1. Filling in the blanks: AI can assume that certain data is irrelevant or insignificant, neglect it, and input the data that seems relevant to the robot.
2. Encouraging better practice: Since chat apps are conditionally widespread now, AI can help the teams more unobtrusively but constantly enhance the quality of the data fed into it.
3. Creating new metadata layers: AI must define more concepts using metadata to gain insight into project status and teams’ productivity.

Such metadata can then be used to train and improve machine learning and provide AI with beneficial advice. When completing the data gaps, AI creators will not be able to compel users to change how they work; on the contrary, they will have to embrace it.

Wrapping it Up! Exciting times ahead For Project Management AI

When new metadata are added, the suitability and quality of data will increase, while a variety of project problems will be well understood. That is why project management AI will be able to provide helpful advice. It is like having AI redesign the tasks for the next few sprints. Knowing how good people are with certain tech and aspects of the system gets your team there a lot faster. That is significant, potent, and helpful. And it’s not far-fetched. Realizing this capability will involve a blend of conventional software engineering work, people’s beliefs on how projects should be executed, and a wide variety of ML and math.

Oh no — not the singularity itself, just a method to better manage projects and teams of people. You wished for this earlier. Can you believe you are getting hours back in your week? To be creative rather than engaged in administrative work for how many minutes or hours? Could you imagine how much more effective you could be on a project if you could eliminate only half of life’s surprise issues? Artificial intelligence in project management will prove instrumental in influencing a team’s performance and the project’s results. It will become as fast as light speed to those teams that adopt and embrace the use of AI in their operations, yet slower than light speed to those counterparts that do not. That is something to look forward to.

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